Abstract:

An apparatus for content item recommendation, such as a Digital Video
Recorder, comprises a grouping unit (105) for grouping user ratings for
content items into rating groups in response to a content item match
criterion. A receiver (109) receives content item data for a plurality of
content items and a first recommendation unit (107) generating a first
set of content item recommendations. An association unit (111) then
determines an associated rating group of the rating groups for each
content item recommendation of the first set and a second recommendation
unit (113) generates a second set of content item recommendations from
the first set in response to a rating group distribution measure for the
second set. The invention may allow improved recommendation of content
items which is aligned with user preferences yet provide a desired
diversity of the provided recommendation. The invention may in particular
provide improved performance for multi-user devices.

Claims:

1. An apparatus for content item recommendation, the apparatus
comprising:a grouping unit for grouping user ratings for content items
into rating groups in response to a content item match criterion;a
receiver for receiving content item data for a plurality of content
items;a first recommendation unit for generating a first set of content
item recommendations;an association unit for determining an associated
rating group of the rating groups for each content item recommendation of
the first set; anda second recommendation unit for generating a second
set of content item recommendations from the first set in response to a
rating group distribution measure for the second set.

2. The apparatus of claim 1 wherein the second recommendation unit is
arranged to select content item recommendations for the second set from
the first set such that the associated rating groups of the second set
meet a rating group distribution criterion.

3. The apparatus of claim 2 wherein the rating group distribution
criterion comprises a requirement that at least one content item is
selected for each rating group for which a content item recommendation is
included in the first set.

4. The apparatus of claim 1 further comprising a unit for adapting a
number of recommendations included in the second set for a first rating
group in response to a user behaviour associated with the first rating
group.

5. The apparatus of claim 4 wherein the user behaviour is a content item
consumption characteristic for content items associated with the first
rating group.

6. The apparatus of claim 1 further comprising a unit for adapting a
number of recommendations included in the second set for a first rating
group in response to a number of user ratings grouped in the first rating
group.

7. The apparatus of claim 1 wherein the first recommendation unit is
arranged to generate a set of content item recommendations for each
rating group in response to content item data and user ratings of that
rating group, and to generate the first set by combining the content item
recommendations of the sets of content item recommendations for each
rating group.

8. The apparatus of claim 1 further comprising a data storage for storing
content items for which content item recommendations are included in the
second set; anda presentation unit arranged to present a content item
from the data storage in response to a user selection of the content
item.

9. The apparatus of claim 1 wherein the second recommendation unit is
arranged to generate a preference value for a plurality of candidate
content item selections from the first set and to select the second set
as the candidate content item selection of the plurality of candidate
content item selections having a highest preference value, the preference
value for a candidate content item selection being a function of user
ratings and a rating group distribution measure for content items of the
candidate content item selection.

10. The apparatus of claim 1 further comprising a user interface for
presenting content item recommendations from the second set, at least one
presentation characteristic for a first content item recommendation being
dependent on an associated user rating group for the first content item
recommendation.

11. The apparatus of claim 1 further comprising a replacement unit
arranged to replace, in the second set, a first content item consumed by
a user by a second content item associated with a same rating group as
the first content item

12. The apparatus of claim 1 wherein the user ratings comprise user
ratings for a plurality of users.

13. The apparatus of claim 12 wherein the user ratings are anonymous.

14. The apparatus of claim 1 arranged to generate content item
recommendations for a plurality of users and wherein the rating groups
are common to a plurality of users

15. The apparatus of claim 1 wherein the content item match criterion
comprises at least one of a content match criterion and a user preference
indication match criterion.

16. A method of content item recommendation, the method
comprising:grouping user ratings for content items into rating groups in
response to a content item match criterion;receiving content item data
for a plurality of content items;generating a first set of content item
recommendations;determining an associated rating group of the rating
groups for each content item recommendation of the first set;
andgenerating a second set of content item recommendations from the first
set in response to a rating group distribution measure for the second
set.

Description:

FIELD OF THE INVENTION

[0001]The invention relates to recommendation of content items and in
particular, but not exclusively, to recommendation of television or radio
programmes.

BACKGROUND OF THE INVENTION

[0002]In recent years, the availability and provision of multimedia and
entertainment content has increased substantially. For example, the
number of available television and radio channels has grown considerably
and the popularity of the Internet has provided new content distribution
means. Consequently, users are increasingly provided with a plethora of
different types of content from different sources. In order to identify
and select the desired content, the user must typically process large
amounts of information which can be very cumbersome and impractical.

[0003]Accordingly, significant resources have been invested in research
into techniques and algorithms that may provide an improved user
experience and assist a user in identifying and selecting content.

[0004]For example, Digital Video Recorders (DVRs) or Personal Video
Recorders (PVRs) have become increasingly popular and are increasingly
replacing conventional Video Cassette Recorders (VCRs) as the preferred
choice for recording television broadcasts. Such DVRs (in the following
the term DVR is used to denote both DVRs and PVRs) are typically based on
storing the recorded television programmes in a digital format on a hard
disk or optical disc. Furthermore, DVRs can be used both for analogue
television transmissions (in which case a conversion to a digital format
is performed as part of the recording process) as well as for digital
television transmissions (in which case the digital television data can
be stored directly).

[0005]Increasingly, devices, such as televisions or DVRs provide new and
enhanced functions and features which provide an improved user
experience. For example, televisions or DVRs can comprise functionality
for providing recommendations of television programs to the user. More
specifically, such devices can comprise functionality for monitoring the
viewing/recording preferences of a user. These preferences can be stored
in a user preference profile and subsequently can be used to autonomously
select and recommend suitable television programs for viewing or
recording. E.g. a DVR may automatically record programs which are then
recommended to the user, for example by inclusion of the automatically
recorded programmes in a listing of all the programmes recorded by the
DVR.

[0006]Such functionality may substantially improve the user experience.
Indeed, with hundreds of broadcast channels providing thousands of
television programs per day, the user may quickly become overwhelmed by
the offering and therefore may not fully benefit from the availability of
content. Furthermore, the task of identifying and selecting suitable
content becomes increasingly difficult and time-consuming. The ability of
devices to provide recommendations of television programs of potential
interest to the user substantially facilitates this process.

[0007]In order to enhance the user experience, it is advantageous to
personalise the recommendations to the individual user. In this context,
a recommendation consists in predicting how much a user may like a
particular content item and recommending it if it is considered of
sufficient interest. The process of generating recommendations requires
that user preferences have been captured so that they can be used as
input by the prediction algorithm.

[0008]There are two main techniques used to collect user preferences. The
first approach is to explicitly obtain user preferences by the user(s)
manually inputting their preferences, for example by manually providing
feedback on content items that the user(s) particularly liked or
disliked. The other approach is to implicitly obtain user preferences by
the system monitoring user actions to infer their preferences.

[0009]Although these techniques may be suitable for many single-user
environments, they are not particularly well suited to many other
environments or to multi-user environments.

[0010]For example, most of the known recommendation approaches are not
ideal in the context of television viewing. A television or video
recorder, such as specifically a DVR, is commonly a multi-user device and
the activity of watching television is characterised by being a low
effort and highly passive activity. In this context, although users ask
for individual recommendations, creating individual user profiles tends
to not be easy or effective.

[0011]Specifically, explicit elicitation of preferences is not effective
as it is difficult for users to precisely describe their tastes.
Furthermore, the user will typically consider it cumbersome and tedious
to manually initialise and maintain a user preference profile.

[0012]Explicit feedback on programmes is impractical in multi user
environments as it requires the user to be identified before the
programme feedback can be recorded in order to allow the system to
differentiate between the preferences of the different users.

[0013]Also, implicit learning of preferences tends not to be effective as
current users would need to be automatically identified and in addition
implicit learning does not work well in contexts such as radio or
television since the radio or television is often used as a background
medium and therefore may play programmes that are not of interest to the
user(s).

[0015]Conventional approaches for such multi-devices specifically tend to
fall into two categories: [0016]1. The device is based on manual
identification of the individual user. Specifically, the device requires
the user to login or otherwise identify themselves to the user. This
identification is used both when entering implicit or explicit
preferences and when obtaining recommendations. [0017]2. The device
considers the users as a single group of homogeneous users. In the
approach, all users are treated identically and recommendations are based
on the preferences of a group as a whole without personalisation for the
individual system. For example, a common user preference profile is used
of all users.

[0018]The first approach is typically incompatible with low user
interaction, casual user activities such as watching television as it
requires inconvenient operations to be frequently performed by the
individual user. Hence, the approach is too cumbersome for many
applications.

[0019]The second approach tends to lead to suboptimal user
personalisation. In particular, it may lead to the preferences of some
users overshadowing the preferences of other users such that the provided
group based recommendations tend to not include sufficient
recommendations for some users.

[0020]Therefore, an improved system for content item recommendation would
be advantageous. In particular, a system allowing an improved user
experience, increased flexibility, reduced complexity, improved
suitability for multi-user environments, reduced need for user inputs,
improved personalisation for the individual user of a multi-user device
and/or improved performance would be advantageous.

SUMMARY OF THE INVENTION

[0021]Accordingly, the Invention seeks to preferably mitigate, alleviate
or eliminate one or more of the above mentioned disadvantages singly or
in any combination.

[0022]According to an aspect of the invention there is provided an
apparatus for content item recommendation, the apparatus comprising: a
grouping unit for grouping user ratings for content items into rating
groups in response to a content item match criterion; a receiver for
receiving content item data for a plurality of content items; a first
recommendation unit for generating a first set of content item
recommendations; an association unit for determining an associated rating
group of the rating groups for each content item recommendation of the
first set; and a second recommendation unit for generating a second set
of content item recommendations from the first set in response to a
rating group distribution measure for the second set.

[0023]The invention may allow an improved recommendation of content items.
Specifically, the invention may e.g. provide increased flexibility and/or
reduced complexity of the recommendation. The invention may allow
improved diversity of the provided recommendations, and may for example
increase the likelihood of the generated recommendations reflecting more
of a users interest and/or interests of more users for a multi-user
implementation. In particular, for a multi user embodiment, the invention
may provide an improved likelihood that the generated recommendations
include recommendations for all users.

[0024]The invention may allow personalised recommendation while ensuring a
desired variety and/or diversity in the generated set of recommendations.

[0025]The invention may allow facilitated implementation and/or operation
in many embodiments. For example, the apparatus may allow efficient
recommendations to be generated based on user ratings provided
anonymously for a plurality of users.

[0026]In particular, the invention may in many embodiments allow improved
multi user recommendations to be generated while allowing a simple
operation and in particular without requiring any identification of the
individual user. For example, the invention may allow content
recommendation in multi-user systems which can reflect individual
preferences for the individual user without requiring that the user
ratings are correlated to specific users or are individually manipulated
for each user.

[0027]The user ratings may e.g. comprise user ratings for a plurality of
users and may be anonymous user ratings. In particular, the user ratings
may be user ratings which comprise no identity information of the
originating user(s) of the user ratings.

[0028]At least some of the user ratings may comprise at least one of
content item description data and preference data (which may be implicit
or explicit).

[0029]The invention may allow facilitated operation and/or an improved
user experience. For example, the invention may allow a flexible and
personalised recommendation without requiring substantial involvement by
the individual users.

[0030]The content items may specifically be television programmes or radio
programmes. The apparatus may specifically be a television, a DVR or a
media server.

[0031]According to another aspect of the invention there is provided a
method of content item recommendation, the method comprising: grouping
user ratings for content items into rating groups in response to a
content item match criterion; receiving content item data for a plurality
of content items; generating a first set of content item recommendations;
determining an associated rating group of the rating groups for each
content item recommendation of the first set; and generating a second set
of content item recommendations from the first set in response to a
rating group distribution measure for the second set.

[0032]These and other aspects, features and advantages of the invention
will be apparent from and elucidated with reference to the embodiment(s)
described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0033]Embodiments of the invention will be described, by way of example
only, with reference to the drawings, in which

[0034]FIG. 1 is an illustration of an example of an apparatus for
generating content item recommendations in accordance with some
embodiments of the invention;

[0035]FIG. 2 is an illustration of an example of data manipulation by the
apparatus of FIG. 1; and

[0036]FIG. 3 is an illustration of an example of a method of generating
content item recommendations in accordance with some embodiments of the
invention.

DETAILED DESCRIPTION OF SOME EMBODIMENTS OF THE INVENTION

[0037]The following description focuses on embodiments of the invention
applicable to a recommendation system for television programmes. However,
it will be appreciated that the invention is not limited to this
application but may be applied to many other content items including any
data entity, stream or file that comprises presentation data for content
that can be presented to a user including for example radio programmes,
audiovisual files, music files etc.

[0038]FIG. 1 is an illustration of a device for making content item
recommendations in accordance with some embodiments of the invention. The
device may for example be a DVR or a television.

[0039]The device of FIG. 1 comprises functionality for recommending
content items to a user. Specifically, the device comprises functionality
for generating recommendations for a user and for storing the recommended
content items at a local storage. Specifically, the device may recommend
television programmes to the user of the device and record these
programmes when they are broadcast. The device uses an approach for
selecting content items to store which is highly flexible, allows a high
degree of personalisation yet ensures that a diverse spectrum of content
items are stored. Specifically, the device uses a two-stage approach
wherein user ratings are first used to generate personalised content
recommendations e.g. for a group of users. A smaller set of
recommendations is then selected while ensuring that sufficient diversity
of the stored content is achieved. E.g. the second stage may seek to
ensure that at least one content item is selected for each user.

[0040]The approach is based around an automatic clustering of user ratings
which may provide a broad diversity of the content items that
nevertheless matches the preferences of the user(s) without any explicit
user identification being necessary. This approach may provide an
efficient implementation with high flexibility and is in particular
useful for multi-user environments wherein, possibly anonymous, user
ratings may be received from a plurality of users.

[0041]The following description will focus on a multi-user device and
scenario such as a DVR used by a plurality of members of a household
(e.g. all members of a family). However, it will be appreciated that the
described principles may also be applied to a single user device.

[0042]The device comprises a user input 101 that can receive manual inputs
from one or more users. Specifically the user input 101 can receive
feedback of the user preferences for various content items. As an
example, a user watching or playing back a specific television programme
can manually input how he rates the program. This rating can be provided
without any identification of the user, i.e. the provided user rating can
be anonymous.

[0043]The user input 101 is coupled to a user ratings store 103. When a
user rating/preference is received from the user input 101, a user rating
record comprising the user preference value and content item
characterising data describing the contents are stored in the user
ratings store 103.

[0044]The user rating record can for example store the user preference as
a number between 1 and 10 as well as characterising (meta)data such as
the genre of the television programme, the title of the television
programme, the duration of the television programme, people involved in
the television programme (such as actors or directors) etc.

[0045]In the example, the device is a multi-user device that may be used
by many different users. Furthermore, the user preferences are inputted
without any identification of the specific user that is providing the
data. Accordingly, the user rating records stored in the user rating
store 103 are anonymous user ratings and the records do not comprise any
information of the identity of the user who provided the input. Thus, the
user ratings are provided without requiring cumbersome or inconvenient
manual user identification. Accordingly, a user can simply use the DVR
without needing to log-in or otherwise identify himself or herself.

[0046]Thus, the DVR collects user preferences which can be explicit (e.g.
a user rates the programme via dedicated buttons on the remote) and/or
can be implicit (e.g. the DVR monitors the users' watching patterns and
infers preferences therefrom). The stored user preferences/ratings are
anonymous and are merged together so that the stored user ratings are a
group of multi-user ratings.

[0047]The user ratings store 103 is coupled to a grouping processor 105
which is arranged to cluster or group user rating records into groups of
user ratings. The grouping of the user ratings is performed in response
to a content item match criterion which may be any suitable match
criterion that allows a grouping of content items into groups having
desirable common characteristics. The match criterion may be a simple
similarity criterion for specific characteristics of the user ratings or
may e.g. be a (potentially complex) clustering algorithm.

[0048]For example, the content item match criterion may require that a
content characteristic, such as a genre or actor, is the same for all the
content items in a given group. Additionally or alternatively, the
content item match criterion may require that user preferences for
content items in the same group are the same or similar. For example, the
grouping processor 105 can generate groups as content items corresponding
to for example movies the users like, movies the users do not like,
actors the users like, actors the users do not like, etc.

[0049]In more complex embodiments, the grouping processor 105 may for
example group the content items by using a clustering algorithm such as a
k-means or isodata clustering algorithm.

[0050]A k-means clustering algorithm initially defines k clusters with
given initial parameters. The user rating records are then matched to the
k clusters. The parameters for each cluster are then recalculated based
on the user rating records that have been assigned to each cluster. The
algorithm then proceeds to reallocate the user rating records to the k
clusters in response to the updated parameters for the clusters. If these
operations are iterated a sufficient number of times, the clustering
converges resulting in k groups of content items having similar
properties.

[0051]In the specific example, the DVR regularly regroups or re-clusters
preferences by similarity in order to create clusters using a clustering
algorithm, such as the K-means algorithm, based on a similarity function
which computes a measure of the similarity of two content items such as
the (weighted) sum of the similarity of their descriptive metadata (e.g.
genre, channel, etc.):

[0052]The metadata can specifically represent content data such that the
similarity measure and the clustering algorithm effectively implement a
content item match criterion which includes a content match criterion.
Thus, the resulting clusters contain content items that have relatively
similar content.

[0053]In some embodiments, the user preference for each content item may
also be taken into account by the clustering algorithm. For example, the
similarity measure may also include a contribution indicating how closely
matched the user ratings for the content items are. Thus, the clustering
algorithm can effectively implement a content item match criterion which
includes a user preference indication match criterion. Thus, the
resulting clusters can contain content items that have been rated
relatively similarly by the users.

[0054]The grouping processor 105 thus generates rating groups by
clustering the user ratings (and associated content items) from the user
ratings store 103. As the user ratings originate from a plurality of
users and are anonymous, the generated user rating groups are common to a
plurality of users.

[0055]The device furthermore comprises a first recommendation processor
107 which is coupled to the grouping processor 105. In addition, the
first recommendation processor is coupled to a content item processor
109. The content item processor 109 receives information of various
content items which are eligible to be recommended to a user and in the
example also receives the broadcast of the content items themselves.

[0056]For example, the content item processor 109 can be provided with
information of the television programmes that are to be received within a
given time interval. Specifically the content item processor 109 can
receive an Electronic Programme Guide (EPG) that indicates the television
programmes that will be transmitted in, say, the next week. In addition
to the time and titles of the television programmes, the EPG can contain
further meta-data such as an indication of the genre, actors, directors
etc.

[0057]The content item processor 109 can specifically include a
conventional television broadcast receiver.

[0058]The first recommendation processor 107 is arranged to generate a
first set of content item recommendations in response to the user
ratings.

[0059]In the specific example, the generation of the first set of
recommendations is based on the rating groups generated by the grouping
processor 105 but it will be appreciated that in other embodiments the
first set may be generated without consideration of these. Indeed, it
will be appreciated that any generation of a set of recommendations may
be used by the first recommendation processor 107 and that this
generation need not necessarily be in response to the user ratings stored
in the user ratings store 103.

[0060]However, in the example, the first recommendation processor 107
initially generates a set of recommendations for each of the user rating
groups determined by the grouping processor 105. Thus, in the example,
the first recommendation processor 107 processes each user rating group
independently of the other user rating groups. For each user rating
group, a list of recommendations is generated.

[0061]Specifically, for each user rating group, the first recommendation
processor 107 compares each of the potential content items from the
content item processor 109 to the characteristics of the user rating
group. If the match is sufficiently close, the content item is considered
to belong to this group and is accordingly considered to have a rating
that can be determined from the user ratings of the group.

[0062]As a simple example, for a given user rating group, a user
preference value can be set to correspond to the average of all the user
preference values for the user rating records in the group. Thus, if the
content item is found to match a group, it is included in the list of
recommendations for that group and is assigned the rating of the group.

[0063]Accordingly, the first recommendation processor 107 generates a
number of recommendation lists with each list comprising a number of
content items that are considered to have characteristics matching the
group.

[0064]Hence, when generating recommendations, the device retrieves the
list of content available for the time period being considered (for
instance via the EPG) and uses the groups to compute recommendations. For
each piece of content, this is done by determining the closest group
(e.g. using a similarity or distance function) and computing the
recommendations for that group using a content matching algorithm and the
programme ratings of this group. This process results in obtaining one
list of recommendations per group. The first set of recommendations can
then simply be determined as the set including all the recommendations
generated, i.e. by combining the recommendations generated for the
individual rating groups.

[0065]It will be appreciated that in practical systems, a complex
recommendation algorithm, such as a reasoning algorithm based on a Naive
Bayes classifier will typically be used.

[0066]The DVR furthermore comprises an association processor 111 which is
coupled to the grouping processor 105 and the first recommendation
processor 107. The association processor 111 is arranged to determine an
associated rating group for each content item recommendation of the first
set of content item recommendations generated by the first recommendation
processor 107.

[0067]The association processor 111 can specifically compare each content
item included in the first set to each of the rating groups and evaluate
a similarity measure for each of the rating groups. It can then
associate/link the content item to the rating group for which the
similarity measure has the highest value. The similarity value used can
specifically be the same similarity value as that used when clustering
the user ratings by the grouping processor 105.

[0068]In the specific example where the first recommendation processor 107
generates a separate list of recommendations for each cluster/rating
group, the association processor 111 can simply link each content item
recommendation of the first set to the rating group which originated the
recommendation.

[0069]In the specific embodiment, each content item recommendation is
associated with only a single rating group. However, it will be
appreciated that in other embodiments, a plurality of rating groups may
be associated with a single content item recommendation. For example, a
content item recommendation may be associated with all rating groups for
which the similarity measure is above a given threshold.

[0070]The first recommendation processor 107 is also coupled to a second
recommendation processor 113. The second recommendation processor 113 is
arranged to generate a second set of recommended content items from the
first set of recommended content items. The generation of the second set
is performed in response to a rating group distribution measure for the
second set.

[0071]The second recommendation processor 113 can specifically select
content item recommendations of the first set for the second set such
that a measure of how broadly the resulting content item recommendations
are distributed across the rating groups. It will be appreciated that any
measure or indication reflecting the distribution of the content item
recommendations of the second set across the rating groups can be used
without detracting from the invention. A simple distribution measure is
the number of rating groups that are represented by the content item
recommendations in the second set. As another example, a distribution
measure which reflects a variance of the number of content item
recommendations of the second set associated with each rating group can
be used. As another example, a distribution measure may be given as the
lowest number of recommendations associated with a rating group.

[0072]As an example of how the second recommendation set may be generated,
the second recommendation processor 113 can be arranged to select a
predetermined number of content item recommendations from the first set
by generating a rating group distribution measure for all possible
selections of recommendations from the first set. The second
recommendation processor 113 can then simply select the content item
selection that results in a distribution measure closest to the desired
value. For example, the selection that results in the highest
distribution measure may be chosen.

[0073]As a more complex example, a preference value may be generated for
each possible selection which takes into account both the user ratings
and the resulting rating group distribution measure. For example, for
each possible selection, an accumulated user rating value may be
calculated for the selected content items and a weighted sum of the
accumulated user rating and the distribution measure may be used as a
preference value. The set of content items resulting in the highest
preference value can then be chosen by second recommendation processor
113. By adjusting the relative weighting of the accumulated user rating
and the distribution measure, a desired trade-off between targeted
personalisation and diversity of the provided recommendations can be
achieved.

[0074]In the example, the second recommendation processor 113 selects the
recommendations for the second set such that a rating group distribution
criterion is met. The rating group distribution criterion may for example
be a requirement that the distribution measure exceeds a given threshold.
Thus the second recommendation processor 113 can select the content item
recommendations of the first set that will result in the highest
accumulated user rating under the conditions that a minimum distribution
across the different rating groups is achieved.

[0075]In some embodiments, the rating group distribution criterion can
comprise a requirement that at least one content item is selected for
each rating group for which a content item recommendation is included in
the first set. Thus, in this example, the recommendations are selected
based on the predicted user interest but are also selected to ensure that
there is at least one programme recorded for each of the identified
rating groups, thereby ensuring a desired degree of diversity and
especially ensuring that all interests represented by a user rating group
(for which the first recommendation processor 107 has generated a
recommendation) are catered for in the resulting second set of
recommendations.

[0076]Thus, the consideration of the rating group distribution when
selecting the recommendations for the second set by the second
recommendation processor 113 allows the system to automatically generate
recommendations which not only reflect the user' preferences but also
provide a desired degree of diversity. Furthermore, this diversity is
aligned along the users' preferences and specifically ensures that
recommendations are provided for a wide variety of specific preferences
represented by the rating groups.

[0077]For example, for a single user DVR, the approach will increase the
likelihood that recommendations are generated for more or all of the
user's interests even if some of these interests have a much higher
rating than other interests. Thus, the system can reduce the probability
that some strong interests completely overshadow weaker interests. E.g. a
user having a very high interest in football matches and a moderate
interest in cars will be provided with recommendations of both television
programmes related to football matches and television programmes related
to cars rather than just programmes related to football matches.
Accordingly, this will allow the user to always have the option of
selecting a television programme suiting his current preference.

[0078]The provided advantages can be even more significant in a multi-user
system. Specifically, in a single user system, the clustering of the
grouping processor 105 results in rating groups aligned along different
preferences of a single user. However, for a multi-user system, the
clustering performed by the grouping processor 105 will also tend to
separate out the individual preferences and interests of different users.
For example, for a DVR used by a family, it is likely that one rating
group will tend to be focused around e.g. children's programmes (of
particular interest to the children of the family), another around e.g.
sports programmes (of particular interest e.g. to the father), another
around news programmes (of particular interest e.g. to the mother),
another around movies (of particular interest e.g. to both parents) etc.

[0079]Accordingly, by generating recommendations such that an increasing
number of clusters or rating groups are catered for, it is ensured that
recommendations are provided for all users. Furthermore, this diversity
can be achieved even if the DVR is predominantly used by only a subset of
the users.

[0080]Hence, in a multi-user environment, the system allows an automatic
alignment of recommendations along the individual user interests and
allows recommendations to be generated which ensure that all users are
sufficiently considered. Furthermore, this is achieved without requiring
any additional identification to be provided by the individual users.
Thus, an improved and facilitated user experience is provided by the
described system.

[0081]The second recommendation processor 113 is coupled to a
recommendation store processor 115 which is furthermore coupled to a
content item store 117. The content item store 117 is coupled to the
content item processor 109 and is fed the content items when these are
received. Specifically, the content item store 117 receives the
television programmes received by the content item processor 109 and the
recommendation store processor 115 controls the content item store 117
such that the television programmes which are recommended in the second
set of recommendations are recorded in the content item store 117. For
this purpose, the content item store 117 may for example comprise a hard
disk.

[0082]The recommendation store processor 115 is furthermore coupled to a
presentation processor 119 and to the user input 101. The recommendation
store processor 115 can, e.g. based on an explicit user request received
by the user input 101, provide the second set of recommendations to the
presentation processor 119. The presentation processor 199 then presents
these recommendations to the user(s). For example, the presentation
processor 119 can display a list of the recommended and stored television
programmes on the display of a television.

[0083]In the specific example, at least one presentation characteristic
for at least one recommendation is dependent on the associated user
rating group(s). For example, the presentation processor 119 can assign a
different font or colour to each user rating group and use this when
presenting the recommendations. As another example, recommendations that
are associated with the same rating group may be grouped together when
presenting the recommendations. Such an approach will provide added
information to the users and for example allow the individual user to
quickly identify recommendations which are likely to be targeted to him
as these may be grouped together.

[0084]In the example, the recommendation store processor 115 can
furthermore receive a selection of one of the recommended content items
from the user input 101 and can in response retrieve the selected content
item from the content item store 117 and present it to the user via the
presentation processor 119.

[0085]In some embodiments, the second recommendation processor 113 is
arranged to adapt the number of recommendations included in the second
set for individual rating groups depending on a user behaviour associated
with the rating groups. Thus, depending on how content items associated
with specific rating groups are consumed or otherwise used by the
user(s), the second recommendation processor 113 may choose to increase
or decrease the number of recommendations included in the second set for
the individual rating groups.

[0086]For example, if the DVR detects that a relatively high number of
content items are selected by the user for a specific rating group, the
second recommendation processor 113 may increase the number of
recommendations selected for that rating group in the future. In
contrast, if content items associated with a specific rating group are
rarely selected by the users, the number of recommendations included for
this group may be reduced.

[0087]Alternatively or additionally, the number of recommendations
included in the second set for a given rating group may be dependent on
the number of user ratings which have been clustered together in this
group. Thus, the number of recommendations for each cluster or rating
group may be dependent on the size of that cluster or group. As the user
ratings in the specific example can be generated implicitly by the user
selecting stored content items, the size of the individual rating group
may be a good indication of how frequently content items of that rating
group are consumed by the users

[0088]In some embodiments, the second recommendation processor 113 is
arranged to replace a recommendation for a content item that has been
consumed by another content item which is associated with the same rating
group as the one that was consumed. Thus, the system may automatically
attempt to replace a consumed content item by a similar content item
thereby continuously providing a set of recommendations that is aligned
with the users preferences and interests.

[0089]It will be appreciated that in some embodiments, the DVR
periodically generates new sets of recommendations as described
previously.

[0090]FIG. 2 illustrates an example of how data may be processed by the
device of FIG. 1.

[0091]The device receives content item data 201 for example in the form of
an EPG. The content item data 201 is compared to the rating groups 203 to
generate a plurality of lists 205. In the specific example, one list of
recommendations 205 is generated for each rating group. In the example, a
first list corresponds to recommended sports programs, a second list
corresponds to recommended soaps and a third list corresponds to
recommended movies. The individual lists 205 are then processed with
reference to the distribution measure or criterion 207. As a result, a
single list of recommendations 209 is generated. This list is
personalised and aligned with the interests of the user(s) yet provides a
high degree of diversity and specifically may cater for the interests of
all users of a multi-user device.

[0092]In the example, the generation of recommended content item lists is
based entirely on information which is not specific or linked to any
individual user but rather is associated with the entire group of users
that use the device. Nevertheless, the system allows a recommendation set
to be generated which reflects the individual preferences of each user.
Furthermore, this processing is performed without requiring that all the
data is processed or sorted according to the individual users thereby
providing a system having low complexity yet generating flexible and
accurate content item recommendation. Specifically, in some embodiments,
the grouping of user ratings and the content item recommendations for
each user rating group may be common for all users.

[0093]The specific described system provides a personalised television
system which successfully fulfils two of the main requirements identified
for users of such systems in that it provides personalised
recommendations for all users of the system yet allows the television
experience to remain casual and simple.

[0094]Furthermore, as preferences are grouped by similarity, one user can
benefit from preferences expressed by another user. This can be a
significant advantage as in many households several persons typically
have overlapping preferences (e.g. the parents). The approach thus
increases the number of preferences available to the system compared to
systems where preferences are kept for individual users and therefore
speeds-up the learning curve of this system and provides more accurate
preferences.

[0095]FIG. 3 illustrates an example of a method of content item
recommendation in accordance with some embodiments of the invention.

[0097]Step 301 is followed by step 303 wherein content item data for a
plurality of content items is received.

[0098]Step 303 is followed by step 305 wherein a first set of content item
recommendations is generated.

[0099]Step 305 is followed by step 307 wherein an associated rating group
of the rating groups is determined for each content item recommendation
of the first set.

[0100]Step 307 is followed by step 309 wherein a second set of content
item recommendations is generated from the first set in response to a
rating group distribution measure for the second set.

[0101]It will be appreciated that the above description for clarity has
described embodiments of the invention with reference to different
functional units and processors. However, it will be apparent that any
suitable distribution of functionality between different functional units
or processors may be used without detracting from the invention. For
example, functionality illustrated to be performed by separate processors
or controllers may be performed by the same processor or controllers.
Hence, references to specific functional units are only to be seen as
references to suitable means for providing the described functionality
rather than indicative of a strict logical or physical structure or
organization.

[0102]The invention can be implemented in any suitable form including
hardware, software, firmware or any combination of these. The invention
may optionally be implemented at least partly as computer software
running on one or more data processors and/or digital signal processors.
The elements and components of an embodiment of the invention may be
physically, functionally and logically implemented in any suitable way.
Indeed the functionality may be implemented in a single unit, in a
plurality of units or as part of other functional units. As such, the
invention may be implemented in a single unit or may be physically and
functionally distributed between different units and processors.

[0103]Although the present invention has been described in connection with
some embodiments, it is not intended to be limited to the specific form
set forth herein. Rather, the scope of the present invention is limited
only by the accompanying claims. Additionally, although a feature may
appear to be described in connection with particular embodiments, one
skilled in the art would recognize that various features of the described
embodiments may be combined in accordance with the invention. In the
claims, the term comprising does not exclude the presence of other
elements or steps.

[0104]Furthermore, although individually listed, a plurality of means,
elements or method steps may be implemented by e.g. a single unit or
processor. Additionally, although individual features may be included in
different claims, these may possibly be advantageously combined, and the
inclusion in different claims does not imply that a combination of
features is not feasible and/or advantageous. Also the inclusion of a
feature in one category of claims does not imply a limitation to this
category but rather indicates that the feature is equally applicable to
other claim categories as appropriate. Furthermore, the order of features
in the claims does not imply any specific order in which the features
must be worked and in particular the order of individual steps in a
method claim does not imply that the steps must be performed in this
order. Rather, the steps may be performed in any suitable order.